For the first experiment we use sat2map dataset and train the CUT model without any modifications using methodology explained above, we will refer to it as CUT - CE.
While this model already surpasses the performance of CycleGAN [1]} by \(\sim \) 5% in terms of FID score (tab:fidscores) and is quite good at translating satellite imagery containing exclusively urban environments (fig:goodexample), severe artifacts and completely incorrect translations can be seen in some edge cases, e.g. in images that contain bodies of water or green surfaces (fig:badexample).
